Optimized migration of virtual objects across environments in a cloud computing environment

ABSTRACT

An appliance migration mechanism (AMM) optimizes migration of a live virtual appliance with virtual objects between cloud environments to minimize temporary connections that use significant cloud resources during the migration. The AMM determines a cost of connections of each virtual object in the virtual appliance and produces an order of migration for the virtual objects. The cost of connections of the virtual objects may be determined by the total number of connections and the maximum number of connections. Alternatively the cost of the connections of the virtual objects for migration could be determined by a weighting the costs of the connections where the weighting may consider loading or other factors on the connection.

BACKGROUND

1. Technical Field

This invention generally relates to cloud computing systems, and morespecifically relates to optimized migration of virtual objects tominimize temporary connections between cloud environments to reduce thecost of resources for the migration.

2. Background Art

Cloud computing is a common expression for distributed computing over anetwork and can also be used with reference to network-based servicessuch as Infrastructure as a Service (IaaS). IaaS is a cloud basedservice that provides physical processing resources to run virtualmachines (VMs) as a guest for different customers. The virtual machinemay host a user application or a server.

A virtual appliance is sometimes described as an application that can bedelivered as a prebuilt unit to execute in a cloud computingenvironment. As used herein, a virtual appliance is set of two or morevirtual objects that work together to provide a service or function toone or more clients in a cloud environment. The virtual objects mayinclude virtual machines, virtual networks, virtual disks or othervirtual appliances. A “live” virtual appliance refers to a virtualappliance that has clients connected to it. It is sometimes desirable tomigrate a live virtual appliance from one environment to another. Theenvironment may be a physical machine, a network, cloud or pool. Theconnected virtual objects that make up the virtual appliance usuallycannot be moved all together in parallel, so the virtual objects aretypically moved one at a time to the new environment. To keep thevirtual appliance live, the system must maintain connections between thevarious virtual machines across the environments during the migration.Maintaining these connections between the environments may requiresignificant resources.

BRIEF SUMMARY

An apparatus and method optimize migration of a live virtual appliancewith virtual objects between cloud environments to minimize temporaryconnections that use significant cloud resources during the migration.An appliance migration mechanism (AMM) determines a cost of connectionsof each virtual object in the virtual appliance and produces an order ofmigration for the virtual objects. The cost of connections of thevirtual objects may be determined by the total number of connections andthe maximum number of connections. Alternatively the cost of theconnections of the virtual objects for migration could be determined byweighting the costs of the connections where the weighting may considerloading or other factors on the connection.

The foregoing and other features and advantages of the invention will beapparent from the following more particular description of preferredembodiments of the invention, as illustrated in the accompanyingdrawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

The disclosure will be described in conjunction with the appendeddrawings, where like designations denote like elements, and:

FIG. 1 is a block diagram of a cloud computing node;

FIG. 2 is a block diagram of a cloud computing environment;

FIG. 3 is a block diagram of abstraction model layers;

FIG. 4 is a block diagram that illustrates an example of a cloud systemwith optimized migration of a virtual appliance as described herein;

FIGS. 5A-5G illustrate an example of migration of a virtual applianceaccording to the prior art;

FIGS. 6A-6G illustrate an example of optimized migration of a virtualappliance as described herein;

FIG. 7 is an example of a recursive computer program for optimizedmigration of a virtual appliance as described herein;

FIG. 8 is a flow diagram of a method for optimized migration of avirtual appliance; and

FIG. 9 is a flow diagram of an example method for step 810 in FIG. 8.

DETAILED DESCRIPTION

The claims and disclosure herein describe an optimize migration of avirtual appliance with virtual objects between cloud environments tominimize temporary connections that use significant cloud resourcesduring the migration. An appliance migration mechanism (AMM) determinesa cost of connections of each virtual object in the virtual applianceand produces an order of migration for the virtual objects. The cost ofconnections of the virtual objects may be determined by the total numberof connections and the maximum number of connections. Alternatively thecost of the connections of the virtual objects for migration could bedetermined by weighting the costs of the connections where the weightingmay consider loading or other factors on the connection.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forloadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a block diagram of an example of a cloudcomputing node is shown. Cloud computing node 100 is only one example ofa suitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 100 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 100 there is a computer system/server 110, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 110 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 110 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 110 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 110 in cloud computing node100 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 110 may include, but are notlimited to, one or more processors or processing units 120, a systemmemory 130, and a bus 122 that couples various system componentsincluding system memory 130 to processing unit 120.

Bus 122 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system/server 110 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 110, and it includes both volatileand non-volatile media, removable and non-removable media. Examples ofremovable media are shown in FIG. 1 to include a Digital Video Disc(DVD) 192 and a USB drive 194.

System memory 130 can include computer system readable media in the formof volatile or non-volatile memory, such as firmware 132. Firmware 132provides an interface to the hardware of computer system/server 110.System memory 130 can also include computer system readable media in theform of volatile memory, such as random access memory (RAM) 134 and/orcache memory 136. Computer system/server 110 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 140 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 122 by one or more datamedia interfaces. As will be further depicted and described below,memory 130 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions described in more detail below.

Program/utility 150, having a set (at least one) of program modules 152,may be stored in memory 130 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 152 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 110 may also communicate with one or moreexternal devices 190 such as a keyboard, a pointing device, a display180, a disk drive, etc.; one or more devices that enable a user tointeract with computer system/server 110; and/or any devices (e.g.,network card, modem, etc.) that enable computer system/server 110 tocommunicate with one or more other computing devices. One suitableexample of an external device 190 is a DVD drive which can read a DVD192 as shown in FIG. 1. Such communication can occur via Input/Output(I/O) interfaces 170. Still yet, computer system/server 110 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 160. As depicted, network adapter 160communicates with the other components of computer system/server 110 viabus 122. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 110. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,Redundant Array of Independent Disk (RAID) systems, tape drives, dataarchival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 200 isdepicted. As shown, cloud computing environment 200 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 210A, desktop computer 210B, laptop computer210C, and/or automobile computer system 210N may communicate. Nodes 100may communicate with one another. They may be grouped (not shown)physically or virtually, in one or more networks, such as Private,Community, Public, or Hybrid clouds as described hereinabove, or acombination thereof. This allows cloud computing environment 200 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 210A-Nshown in FIG. 2 are intended to be illustrative only and that computingnodes 100 and cloud computing environment 200 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 200 in FIG. 2 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and the disclosure andclaims are not limited thereto. As depicted, the following layers andcorresponding functions are provided.

Hardware and software layer 310 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM System z systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM System p systems; IBMSystem x systems; IBM BladeCenter systems; storage devices; networks andnetworking components. Examples of software components include networkapplication server software, in one example IBM WebSphere® applicationserver software; and database software, in one example IBM DB2® databasesoftware. IBM, System z, System p, System x, BladeCenter, WebSphere, andDB2 are trademarks of International Business Machines Corporationregistered in many jurisdictions worldwide.

Virtualization layer 320 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 330 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA. The management layer further includes anapplication migration mechanism (AMM) 350 as described herein. While theAMM 350 is shown in FIG. 3 to reside in the management layer 330, theAMM 350 actually may span other levels shown in FIG. 3 as needed.

Workloads layer 340 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing and mobile desktop.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As introduced above, when migrating a virtual appliance between cloudenvironments in the prior art, connected virtual objects of theappliance are typically moved one at a time to the new environment. Tokeep the virtual appliance live, the system maintains the connectionsbetween the various virtual machines across the environments during themigration. The disclosure and claims herein described a method andapparatus to optimize the migration of a virtual appliance between cloudenvironments to minimize the temporary connections and thus theirassociated connection costs to the system. The optimized migration ofthe virtual appliance as described herein is performed by the appliancemigration mechanism (AMM) introduced with reference to FIG. 3.

Referring now to FIG. 4, a block diagram illustrates a multiple cloudsystem 400 for optimized migration of a virtual appliance between cloudenvironments. The cloud system 400 includes a cloud manager 410 thatmanages one or more clouds. Except as described herein, the cloudmanager may function in the same manner as cloud managers known in theprior art. In this example, the application migration mechanism (AMM)350 is incorporated into the cloud manager 410. The AMM determines acost of connections of each virtual object in the virtual appliance andproduces an order of migration for the virtual objects that will reducethe connection costs during migration.

Referring again to FIG. 4, in this example the cloud manager 410 managesa first cloud 412 and a second cloud 414. The first cloud 412 includes avirtual appliance 416. The virtual appliance 416 comprises virtualobject A 418, virtual object B 420 and virtual object C 422. Virtualobject A has a connection 424 to virtual object B 420. Virtual object B420 has a connection 426 to virtual object C 422. The connections 424,426 may be identified in the definition of virtual appliance 416 forexample as defined in various properties and initialization files.Alternatively, these connections may be identified by looking at thepatterns of communication on the network and inferring connectionrelationships.

In the example shown in FIG. 4, the AMM will optimize the migration thevirtual appliance 416 from the first cloud 412 to the second cloud 414.The AMM determines the resource costs of connections between the virtualobject in the virtual appliance for the optimization. In this example,the AMM monitors 428 the connections 424, 426 between the virtualobjects. The AMM may store the connection cost information determined bymonitoring the connections in historical data 430. This allows the AMMto use connection cost data over various time periods in calculatingcosts of connections. To determine connection costs, the AMM may monitorthe physical traffic on the network over network connections 424, 426via any known network monitoring technology. For example, the AMM maycount physical packets, and look at from/to data in the headers.Alternatively, the AMM may query virtual objects 418, 420, and 422 andinquire how much data they have placed on the connections 424, 426 in aspecified time period (for example the last minute, or ten minutes,etc). Additionally, the AMM may query hardware supporting theconnections (routers, switches, hubs, etc) to request a maximumtransmission rate, or an average transmission rate over a specified timeperiod.

FIGS. 5A through 5G illustrate an example of migrating a virtualappliance according to the prior art. In this simplified example, afirst cloud 510 includes a virtual appliance that comprises virtualobject A 514, virtual object B 516 and virtual object C 518. It isunderstood that a virtual appliance may actually contain many morevirtual objects with many more connections. FIGS. 5B through 5G show thesteps to move the virtual appliance from the first cloud 510 to thesecond cloud 512. In this example, the system randomly selects to movevirtual object B 516 first. FIG. 5B illustrates the migration of virtualobject B 516 to the second virtual cloud 512. The migration of virtualobject B 516 to the second virtual cloud is done over a connection 520in the manner know in the prior art. This type of connection will bereferred to as a migration connection. The migration connection 520 mayrequire significant cloud and network resources if virtual object B isof substantial size.

FIG. 5C illustrates the example after the migration of virtual object B516 has been completed. At this point virtual object B 516 is executingin cloud 512. The cloud manager maintains the connections 522, 524 tovirtual object B 516 in the manner known in the prior art so that theappliance can continue execution without interruption. The connections522, 524 to virtual object B are communication connections. Theseconnections may require very little or substantial network resourcesdepending upon the application. Next the system determines to migratevirtual object A 514. The migration of virtual object A 514 to thesecond virtual cloud 512 is done over a migration connection 526 asshown in FIG. 5D. When the migration of virtual object A is complete thesystem will appear as shown in FIG. 5E. Next, FIG. 5F shows that thesystem finally proceeds to migrate virtual object C 518. The migrationof virtual object C 518 to the second virtual cloud 512 is done over amigration connection 528. When the migration of virtual object C iscomplete the system will appear as shown in FIG. 5G, where all thevirtual objects in cloud 510 have been migrated to cloud 512.

It is important to note the total cross environment connections in theexample shown in FIGS. 5A through 5G. Cross-environment connections asused herein means connections between objects in the two differentclouds. In this example, if we total the cross environment connectionsin each step, there are a total of nine connections made to migrate thevirtual appliance from the first cloud 510 to the second cloud 512.Three of the connections are of the migration type (520, 526, 528) andsix of the connections are of the communication type (522, 524 countedat each step). The communication type connections are recounted at eachstep for comparison purposes. While they are actually the sameconnection, recounting them gives a count that reflects the use ofresources as these connections continue to be open and carrying datatraffic during each step. Also of particular note is the maximum numberof cross cloud/environment connections at any one time is three as shownin FIG. 5D.

FIGS. 6A through 6G illustrate an example of optimized migration of avirtual appliance between cloud environments. Again this is a simplifiedexample for illustration. A first cloud 610 includes a virtual appliancethat comprises virtual object A 614, virtual object B 616 and virtualobject C 618. FIGS. 6B through 6G show the steps executed by the AMM tooptimize the migration of the virtual appliance from the first cloud 610to the second cloud 612. The AMM optimizes the migration by determiningto first move virtual object C 516. The manner of determining the orderof migration is described further below. FIG. 6B illustrates themigration of virtual object C 618 to the second virtual cloud 612. Themigration of virtual object C 618 to the second virtual cloud is doneover a migration connection 620. FIG. 6C illustrates the example afterthe migration of virtual object C 618 has been completed. At this pointvirtual object C 618 is executing in cloud 512. The cloud managermaintains the single connection 622 to virtual object C 618 so that theappliance can continue execution without interruption. Next the AMMdetermines as described below to migrate virtual object B 616. Themigration of virtual object B 616 to the second virtual cloud 612 isdone over a migration connection 624 as shown in FIG. 6D. When themigration of virtual element B is complete the system will appear asshown in FIG. 6E. Next, FIG. 6F shows that the AMM finally proceeds tomigrate virtual object A 614. The migration of virtual object A 614 tothe second virtual cloud 512 is done over a migration connection 628.When the migration of virtual element A is complete the system willappear as shown in FIG. 6G.

Again referring to FIGS. 6A through 6G, the total cross environmentconnections in the optimized migration is less than the prior artexample. In this example, there are a total 7 cross environmentconnections made to migrate the virtual appliance from the first cloud610 to the second cloud 612. Again there are three migration typeconnections (620, 624, 628). However there are only 4 communication typeconnections counted at each step. Most advantageously, the maximumnumber of connections at any one time is two as shown in FIGS. 6D and6F. By optimizing the order of migration the number of connections madeand the maximum number of connections at any point in time is reducedfrom the prior art example.

The AMM optimizes migration of a virtual appliance with virtual objectsbetween cloud environments to minimize cross environment connections.The appliance migration mechanism (AMM) seeks to determine an optimizedorder to migrate the virtual objects. For example, the AMM may determineor estimate a cost for connections for moving each virtual object in thevirtual appliance. The AMM may then compare the costs and produce anorder of migration for the virtual objects based on the lowest cost forthe migration. The AMM then moves or directs other entities in the cloudmanager 410 (FIG. 4) to move the virtual objects to the new cloud in thedetermined order. The cost of the cross environment connections may bedetermined in various ways. For example, the cost of cross environmentconnections during migration may be estimated by the total number ofconnections and the maximum number of connections as illustrated in theprevious example in FIGS. 6A through 6G. The total number of connectionsand the maximum number of connections can be used singularly or incombination to determine the cost of connections.

FIG. 7 is an example of a recursive computer program 700 to optimizemigration of a virtual appliance. Program 700 determines an optimizedorder to migrate the virtual objects by estimating the cost for movingeach virtual object based on the number of connections or the maximumnumber of connections. The program initializes a list for the initialcloud (UnMigratedVMs) with all virtual objects of the virtual appliancethat will be migrated. A list for the target cloud that is initiallyempty is also created. The program then calls a recursive routine toconsider first moving each virtual object in the list. The program thenselects an object and moves it to the target cloud list and determineshow many connections are drawn to the target cloud by the move. Anobject connected to the first object is then moved until all the objectshave been moved while adding the total connections and store the maximumnumber of connections for each object. The recursive routine adds thetotal number of connections and stores the maximum number of connectionsat any one time. The AMM uses the total number of connections and themaximum number of connections to determine the virtual object with theleast cost in connections to move first. The recursive routine candetermine the optimal order to migrate the virtual objects by onlykeeping the list with the lowest cost based on the number ofconnections.

In another example for optimized migration of a virtual appliance, theAMM determines the cost of the connections by weighting each of theconnections between the virtual object. In the previous example theweighting costs of the connections were essentially assumed to be equaland just added together. In this example, the computer program in FIG. 7would aggregate a weighting cost by adding a connection weighting factorfor each connection found. The weighting factor could be calculated as apercentage of an interface bandwidth that the cross environmentconnection will require. The AMM would then choose the virtual objectwith connections with the lowest total weighting factor that representsthe lowest percentage of the connection bandwidth between the virtualobjects. The weighting of the connections could consider current and/orhistorical loading or other factors of the cross environment connection.The historical connection cost or loading of the connections is storedby the AMM in the historical data 430 (FIG. 4).

In another example for optimized migration of a virtual appliance, theAMM could optionally move multiple virtual objects. In the aboveexamples, the AMM only moved a single virtual object. If the system hasthe capability and if the virtual objects are small enough, the VMMcould choose a group or subset of the virtual objects to move togetherand then determine the costs of connections between the chosen group andthe virtual objects remaining at the original environment.

FIG. 8 illustrates a flow diagram of a method 800 for optimizedmigration of a virtual appliance with virtual objects between cloudenvironments. The method 800 is presented as a series of steps performedby a computer software program such as the appliance migration mechanism350 described above. First, find the cost of connections for moving eachof the virtual objects in an appliance (step 810). Compare the costs ofmoving the virtual objects (step 820). Determine an order to migrate thevirtual objects based on the compared costs (step 830). Move the virtualobjects to a new cloud according to the determined order (step 840). Themethod is then done.

Referring now to FIG. 9, a flow diagram shows method 900 that is anexemplary method for performing step 810 in method 800. The method 900is presented as a series of steps performed by a computer softwareprogram described above as the application migration mechanism 350.First, create a list of objects for the initial cloud and a list for thetarget cloud (the target cloud list is initially empty) (step 910).Randomly select an object and move it to the target cloud list (step920). Determine how many connections are drawn to the target cloud bythe move (step 930). Move an object connected to the randomly selectedobject until all the objects have been moved (step 940). Add the totalconnections and store the maximum number of connections for the selectedobject (step 950). Return to step 920 and repeat for each virtual objectremaining in the initial cloud list (step 960). The method is then done.

The claims and disclosure herein provide an apparatus and method foroptimized migration of a live virtual appliance with virtual objectsbetween cloud environments. The appliance migration mechanism determinesa cost of connections of each virtual object in the virtual applianceand produces an order of migration for the virtual objects to minimizetemporary connections that use significant cloud resources during themigration.

One skilled in the art will appreciate that many variations are possiblewithin the scope of the claims. Thus, while the disclosure isparticularly shown and described above, it will be understood by thoseskilled in the art that these and other changes in form and details maybe made therein without departing from the spirit and scope of theclaims.

The invention claimed is:
 1. A computer-implemented method executed by at least one processor for optimizing migration of a live virtual appliance having a plurality of virtual objects from a first virtual environment to a second virtual environment, the method comprises: determining a cost of connections for moving each virtual object; comparing the cost of connections for moving each of the virtual objects; determining from the cost of connections for moving each of the virtual objects a migration order for the plurality of virtual objects of the live virtual appliance comprising: creating a list of objects for an initial cloud and a list for a target cloud; repeatedly selecting a first object from the initial cloud and moving it to the target cloud and performing the following steps until each virtual object in the initial cloud is moved to the target cloud: determining how many connections are drawn to the target cloud; moving an object connected to the first object and add the connections required to a total connections and store a maximum connections; repeatedly moving objects connected to the selected first object until all objects of the virtual appliance have been moved to the target cloud; and moving the plurality of virtual objects of the live virtual appliance to the second virtual environment in the order of the determined migration order.
 2. The method of claim 1 further comprising monitoring connections among the plurality of virtual objects to determine a connection cost.
 3. The method of claim 2 further comprising storing a historical connection cost for the monitored connections among the plurality of virtual objects.
 4. The method of claim 1 wherein determining the cost of connections of the virtual objects comprises aggregating a weighting factor for each connection for the plurality of virtual objects.
 5. The method of claim 4 further comprising determining the weighting factor for each connection by a percentage of the network bandwidth used by the connection.
 6. The method of claim 1 wherein the cost of connections is determined for a plurality of subsets of the plurality of virtual objects in the virtual appliance from the total number of connections and a maximum number of connections for each of the subsets.
 7. The method of claim 1 wherein the first virtual environment and the second virtual environment comprise a virtual cloud.
 8. A computer-implemented method executed by at least one processor for optimizing migration of a live virtual appliance having a plurality of virtual objects from a first virtual cloud to a second virtual cloud, the method comprises: determining a cost of temporary connections for moving each virtual object; comparing the cost of temporary connections for moving the virtual objects; determining from the cost of temporary connections for moving each of the virtual objects a migration order for the plurality of virtual objects of the live virtual appliance comprising: creating a list of objects for an initial cloud and a list for a target cloud; repeatedly selecting a first object from the initial cloud and moving it to the target cloud and performing the following steps until each virtual object in the initial cloud is moved to the target cloud; determining how many connections are drawn to the target cloud; moving an object connected to the first object and add the connections required to a total connections and store a maximum connections; and repeatedly moving objects connected to the selected first object until all objects of the virtual appliance have been moved to the target cloud; moving the plurality of virtual objects of the live virtual appliance to the second virtual cloud in the order of the determined migration order; and monitoring connections between the plurality of virtual objects to determine a connection cost and storing a historical connection cost for the monitored connections between the plurality of virtual objects.
 9. The method of claim 8 wherein determining the cost of connections of the virtual objects comprises aggregating a weighting factor for each connection for the plurality of virtual objects determined by the percentage of the network bandwidth used by the connection.
 10. The method of claim 8 wherein the cost of connections is determined for a plurality of subsets of the plurality of virtual objects in the virtual appliance from the total number of connections and a maximum number of connections for each of the subsets.
 11. A computer-implemented method executed by at least one processor for optimizing migration of a live virtual appliance having a plurality of virtual objects from a first virtual environment to a second virtual environment, the method comprises: determining a cost of temporary connections for moving each virtual object during migration of the live virtual appliance from first virtual environment to the second virtual environment to optimize the migration; comparing the cost of temporary connections for moving each of the virtual objects; determining from the cost of temporary connections for moving each of the virtual objects a migration order for the plurality of virtual objects of the live virtual appliance; and moving the plurality of virtual objects of the live virtual appliance to the second virtual environment in the order of the determined migration order.
 12. The method of claim 11 further comprising monitoring connections among the plurality of virtual objects to determine a connection cost.
 13. The method of claim 12 further comprising storing a historical connection cost for the monitored connections among the plurality of virtual objects.
 14. The method of claim 11 further comprising: creating a list of objects for an initial cloud and a list for a target cloud; repeatedly selecting a first object from the initial cloud and moving it to the target cloud and performing the following steps until each virtual object in the initial cloud is moved to the target cloud: determining how many connections are drawn to the target cloud; moving an object connected to the first object and add the connections required to a total connections and store a maximum connections; and repeatedly moving objects connected to the selected first object until all objects of the virtual appliance have been moved to the target cloud.
 15. The method of claim 11 wherein determining the cost of connections of the virtual objects comprises aggregating a weighting factor for each connection for the plurality of virtual objects.
 16. The method of claim 15 further comprising determining the weighting factor for each connection by a percentage of the network bandwidth used by the connection.
 17. The method of claim 11 wherein the cost of connections is determined for a plurality of subsets of the plurality of virtual objects in the virtual appliance from the total number of connections and a maximum number of connections for each of the subsets.
 18. The method of claim 11 wherein the first virtual environment and the second virtual environment comprise a virtual cloud. 